Browsing by Author "Sergieieva, Kateryna L."

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Analysis of Relationships between Parameters of the National Forest Inventory of Finland: Case Study of Mesic Forest
(Geological Society Publishing House, London, UK, 2025) Kavats, Olena O.; Khramov, Dmitriy; Sergieieva, Kateryna L.
ENG: The use of satellite images and machine learning in addition to in situ data in national forest inventories enables covering large areas and significantly reduces costs. However, such combined inventories provide modelled stand properties, the relationships between which are not well understood. An approach to investigating linear and non-linear relationships between forest inventory parameters is proposed. It is applied to a study of the Multi-Source National Forest Inventory (MS-NFI) stand properties for the case of mesic forests. The relationships between MS-NFI parameters and stand reflectance in the visible, red edge, near infrared and short-wave infrared spectral regions were investigated for the Sentinel-2 satellite sensor. Linear models of canopy reflectance as a function of forest stand and elevation properties were developed. These models allowed to assess the comparative influence of MS-NFI parameters on stand reflectance as well as the monthly dynamics of this influence during the season (May–August 2019). Linear relationships between forest inventory parameters were investigated using a correlation matrix. Generalized additive models were used to investigate non-linear pairwise relationships between forest inventory parameters. The proposed approach can be applied to assess the impact of stand features obtained from conventional ground-based forest inventory on forest canopy reflectance.
Information System for Abandoned Arable Land Detection From Sentinel-2 Images
(CEUR-WS Team, Aachen, Germany, 2025) Akymenko, Karyna; Sergieieva, Kateryna L.; Kavats, Yurii V.; Kovrov, Oleksandr S.
ENG: An information system for the automated detection of abandoned arable land, based on Sentinel-2 satellite images, is developed. The system provides monitoring of agricultural land, even in areas where ground surveys are challenging to conduct. Integrated with Google Earth Engine (GEE), the system classifies agricultural areas as cultivated or abandoned in near real time based on Normalized Difference Vegetation Index (NDVI) time series. It supports two modes of operation: local analysis of GeoTIFF files and cloud analysis using an interactive map. Its classification method compares the maximum NDVI values for the target and reference years, enabling the detection of the characteristics of the vegetation cover degradation of abandoned land. The results were experimentally validated for a sample of agricultural areas in the Dnipropetrovsk and Donetsk Oblasts. The proposed system can detect abandoned arable land with an accuracy of up to 92.5% (F1-score: 0.898), even in areas of military conflict where ground observations are unavailable.
Open Access Satellite Data for Global Greenhouse Gas Monitoring
(Український державний університет науки і технологій, ННІ «Інститут промислових та бізнес технологій», 2022) Kavats, Olena O.; Khramov, Dmitriy A.; Sergieieva, Kateryna L.; Vasyliev, Volodymyr V.
ENG: Open satellite concentration data for the main greenhouse gases (CO2, CH4, N2O) are considered in terms of their possible use for local, regional, and global monitoring. The main data characteristics are provided. The satellite products most suitable for global moni-toring of greenhouse gas concentrations are specified. The disadvantages of existing satellite data are analyzed.
Open-pit Mining Activity and Stability Area Mapping in the Pyhäsalmi Mine Using a Time Series of Sentinel-1 Images
(Geological Society Publishing House, London, UK, 2026) Kavats, Olena O.; Khramov, Dmitriy; Sergieieva, Kateryna L.
ENG: Synthetic aperture radar (SAR) single look complex (SLC) Sentinel-1 data provide continuous all-weather monitoring of the Earth's surface and detection of texture change areas based on coherence maps. A mining intensity assessment method is proposed in this paper to identify activity and stability areas by calculating the temporal activity index (TAI) – the frequency indicator of surface changes. The TAI value was calculated from a time series of coherence-based normalized differential activity index (NDAI) robust to baseline and temporal decorrelation. The method was applied to monitor the surface conditions of the Pyhäsalmi Mine's (Finland) open pits and waste dumps from May to September 2018–2022, using a time series of Sentinel-1A images acquired in vertical transmit and vertical receive (VV) polarization with a 12-day time step. In each of the observation seasons, TAI maps were generated and areas of activity were identified, mainly on the surface of the waste dumps, at the bottom of the backfill open pit and on the slopes of the old open pit. Activity and stability areas were validated using unmanned aerial vehicle (UAV)-based very high spatial resolution visual orthomosaics and digital surface models (DSMs), which confirmed land cover changes in areas with high TAI values and no changes in stability areas.